How to Predict Daily High Temperatures: A Practical Guide

Predicting tomorrow's high temperature is one of the oldest problems in weather science. The professional answer involves coupled atmospheric models running on supercomputers. The practical answer — for someone who wants to make a useful prediction without a PhD — is much simpler.

This guide walks through how to combine three independent signals into a single prediction with quantified uncertainty.

The three inputs that matter

Any sensible temperature prediction combines:

1. Climatology — what's typical for this date at this location
2. Forecast model output — what NWS (or other models) predicts
3. Live observations — what's actually happening today

Each one has different strengths. Combining them outperforms any single source.

Climatology: the baseline

Climatology is the 30-year average high temperature for a given month at a given station. For example, Miami in July has a climatological mean of ~90.5°F with a standard deviation of ~2.0°F.

This means: in a typical July, daily highs cluster tightly around 90.5°F. 68% of days fall between 88.5°F and 92.5°F. Extreme outliers (above 95°F or below 85°F) are rare.

Climatology data is publicly available from NOAA. For predictive use, you want:

• The monthly mean for the target date
• The monthly standard deviation (variance)

This gives you a base estimate before you know anything specific about today.

Forecast model output: the anchor

NWS publishes daily forecasts via their National Digital Forecast Database (NDFD). These reflect millions of dollars of computational modeling — they're the best public input you have.

Forecast accuracy at 24-hour horizon (per the earlier post on this blog) is:

• Miami: ~2°F RMSE
• Los Angeles: ~3°F RMSE
• New York/Chicago: ~4°F RMSE

So forecasts are useful but imperfect. Treat them as a signal, not gospel.

Live observations: the truth in progress

By 8 AM local time, you already know:

• Current temperature
• Dewpoint
• Wind speed and direction
• Sky condition (cloudy, clear, etc.)
• The morning's observed peak so far

These observations carry real information about today specifically that climatology and yesterday's forecast can't capture.

Combining the signals

Here's a practical weighting scheme that works well in practice:

Morning (before 7 AM) — observations are minimal. Use:

• 65% forecast model
• 35% climatology
• 0% observations

Midday (10 AM - 2 PM) — observations carry real information:

• 25% forecast model
• 15% climatology
• 60% (observed peak so far + expected remaining heating)

Afternoon (3 PM onward) — peak is usually near or already reached:

• 10% forecast model
• 10% climatology
• 80% (observed peak + small remaining upside)

The weights shift smoothly through the day as observations accumulate.

The "remaining heating" component

The afternoon component requires estimating how much more heating remains from now until end of day. This depends on time of day and weather regime.

A simplified table for a normal summer day at Miami:

| Local hour | Mean remaining heating | Sigma | |---|---|---| | 7 AM | +9°F | 2.5°F | | 9 AM | +6°F | 2.0°F | | 11 AM | +2.5°F | 1.5°F | | 1 PM | +0.7°F | 1.0°F | | 3 PM | +0.1°F | 0.5°F | | 5 PM | 0°F | 0.4°F |

So if it's 11 AM and you've observed 87°F so far in Miami, your remaining heating estimate is 87 + 2.5 = 89.5°F ± 1.5°F.

Different regimes have different tables. Santa Ana days at LA show much more afternoon heating than marine cloudy days, for example.

Adjusting for the weather regime

Beyond climatology, daily weather patterns ("regimes") matter:

Marine breeze: onshore winds suppress heating. Common at coastal stations. Continental flow: dry inland air boosts heating. Frontal passage: sharp cooling, especially in spring/fall. Stagnant high pressure: clear skies, low wind, often above normal heating.

Each regime nudges the prediction up or down. At KMIA, an "offshore" N/NW wind in winter is actually a cool front (post-frontal), not the warm dry boost you'd expect by analogy with West Coast Santa Ana winds. The same wind direction means opposite things at different stations.

This is why generic temperature prediction rules (like "dry winds = hotter") fail — they need to be station-specific.

Quantifying uncertainty (the sigma)

A prediction is incomplete without a measure of confidence. Good practice: give a mean (μ) and standard deviation (σ).

For example: "Tomorrow's high will be 88.5°F ± 2.0°F."

This says: 68% of the time, the actual high should fall within 86.5°F to 90.5°F. The σ shrinks throughout the day as observations confirm or contradict the morning prediction.

Sigma sources to combine:

• Climatological sigma (wide — captures all possible days)
• Forecast model uncertainty (medium)
• Observation-based uncertainty (narrow once peak is set)

Weight these by the same morning/midday/afternoon schedule as the means.

A common pitfall: ignoring recent bias

If yesterday's high was 4°F above climatology, and the day before too, you're in a regime that the climatology baseline doesn't capture. Smart predictions adjust for this.

A simple approach: compute the residual (actual − climatology) for the last 5-10 days. The average of those residuals is your "recent bias." Add ~70% of it to your climatology component.

But — be careful with small samples. 5 days of data can be noisy. Bayesian shrinkage toward zero helps: weight the recent bias proportional to sample size.

Where these predictions still fail

After all this, predictions still miss on:

Frontal timing errors — front arrives 4 hours late, peak shifts by 6°F. Surprise convection — thunderstorm at 2 PM kills the afternoon heating. Regime transitions — first day of a new pattern, recent bias is wrong.

These aren't bugs in the method — they're inherent forecast horizons. Even the best models can't predict thunderstorm timing 24 hours out.

The right framing: a good prediction is useful 75-80% of days, marginal on 15%, and quietly wrong on 5%. Risk management matters more than always being right.

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